A searchable data catalog that facilitates researchers'' access to large datasets available either publicly or through institutional or individual licensing. Dataset records include information about the content of the dataset, how to access the dataset, and local experts within NYULMC and NYU to assist in the use of these datasets. The data catalog will expand to include internally generated datasets from NYULMC and NYU in the near future. Use the contact form if you are interested in submitting a dataset to the data catalog.

The Nationwide Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest all-payer inpatient health care database in the United States, yielding national estimates of hospital inpatient stays. The NIS can be used to identify, track, and analyze national trends in health care utilization, access, charges, quality, and outcomes. Data may not be available for all states across all years.

Traffic analytics, rankings, and competitive metrics for nyu.edu as of September 2025

Comprehensive dataset containing 2 verified Public library businesses in Nyu District, Fukui, Japan with complete contact information, ratings, reviews, and location data.

Context: The NYU-Depth-V2 dataset was developed to enhance indoor scene understanding through RGB-D inputs, containing 407,024 frames of RGB images paired with depth maps. It includes 894 object categories and is designed for multi-class segmentation tasks.

Sources: The dataset is based on the work of Silberman and Fergus, which aimed to improve label categorization and increase the dataset's size, providing a rich resource for training and evaluating segmentation models.

Inspiration: The dataset was inspired by the need for robust feature learning in computer vision, particularly in recognizing and segmenting various indoor scenes, which has applications in robotics and augmented reality.

Mentions:

• An original paper where this dataset is first mentioned - is "Indoor Semantic Segmentation using depth information".
• This depth-estimation dataset is also mentioned in Depth Anything V1 and Depth Anything V2 papers.

The Statewide Planning and Research Cooperative System (SPARCS) Inpatient De-identified File contains discharge level detail on patient characteristics, diagnoses, treatments, services and charges. This data file contains basic record level detail for the discharge. The de-identified data file does not contain data that is protected health information (PHI) under HIPAA. The health information is not individually identifiable; all data elements considered identifiable have been redacted. For example, the direct identifiers regarding a date have the day and month portion of the date removed. For more information, check out: http://www.health.ny.gov/statistics/sparcs/, and go to "About" tab.

Comprehensive dataset containing 1 verified Film and photograph library businesses in Nyu District, Fukui, Japan with complete contact information, ratings, reviews, and location data.

The dataset includes the names, employee sizes, asset sizes, business credit score, owner information, address, longitude and latitude, and census tract information for all businesses in New York City from 2010 to 2014. For nursing homes and hospitals, the dataset also categorizes capacity by the number of beds.

The Congressional District Health Dashboard (CDHD) was launched in 2023 to present actionable and nonpartisan data on health, drivers of health, and health equity for all 435 congressional districts across the United States and Washington DC. Like the City Health Dashboard, available metrics include health outcomes, social and economic factors, health behavior, physical environment, and clinical care to enable policymakers, advocates, and community members to identify their strengths and challenges and drive positive change.

The CDHD incorporates the 2022 re-drawn district boundaries based on the 2020 census and corresponding to the 118th Congress (beginning January 2023).

In these experiments we bring together research on active learning and intuitive physics to explore how people actively about physical properties in "microworlds" with continuous spatiotemporal dynamics. In two experiments, participants interacted with objects in simulated two-dimensional microworlds governed by a real-time physics engine, with the goal of identifying latent physical properties of the objects in the scenes, such as their masses, and forces of attraction or repulsion. We find an advantage for active learners over passive and yoked controls, and show that active learners generate evidence specific to whatever physical property it is their goal to identify. Consequently, yoked learners do better when asked to identify the same property. Our active participants spontaneously performed various "natural experiments" which revealed the objects' properties with varying success. In our research papers we highlight, and begin to and formalize these experiments, and finally outline further steps to categorize and explore active learning in the wild.

Dataset

This dataset was created by Bianca Ghx

Contents

All 311 Service Requests from 2010 to present. This information is automatically updated daily.

Nyu Institute Study Ancient World Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.

Overview

The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It features:

• 1449 densely labeled pairs of aligned RGB and depth images
• 464 new scenes taken from 3 cities
• 407,024 new unlabeled frames
• Each object is labeled with a class and an instance number (cup1, cup2, cup3, etc)

The dataset has several components:

• Labeled: A subset of the video data accompanied by dense multi-class labels. This data has also been preprocessed to fill in missing depth labels.
• Raw: The raw RGB, depth, and accelerometer data as provided by the Kinect.
• Toolbox: Useful functions for manipulating the data and labels.

Note: This instance only consists of the labeled dataset collected from https://cs.nyu.edu/~silberman/datasets/nyu_depth_v2.html.

Cambrian-Alignment Dataset

Please see paper & website for more information:

https://cambrian-mllm.github.io/ https://arxiv.org/abs/2406.16860

  Overview

Cambrian-Alignment is an question-answering alignment dataset comprised of alignment data from LLaVA, Mini-Gemini, Allava, and ShareGPT4V.

  Getting Started with Cambrian Alignment Data

Before you start, ensure you have sufficient storage space to download and process the data.

Download the Data Repository… See the full description on the dataset page: https://huggingface.co/datasets/nyu-visionx/Cambrian-Alignment.

This dataset provides information about the number of properties, residents, and average property values for NYU Place cross streets in Murfreesboro, TN.

The "https://www.nyu.edu/" Target="_blank">New York University Science and Religion Survey is a nationally representative survey conducted by "https://www.norc.org" Target="_blank">NORC using their "https://www.norc.org/services-solutions/amerispeak.html" Target="_blank">AmeriSpeak Panel. The survey focuses on Americans' attitudes towards both science and religion, including items about confidence, identities, and policy preferences. A series of questions explores Americans' discussion networks for topics related to science and religion. Another set asks churchgoers about actions (if any) that their church took in response to the COVID-19 pandemic. Additional variables ask about respondents' political views, demographic characteristics and use of various social media platforms.

The National Center for Advancing Translational Sciences (NCATS) has systematically compiled clinical, laboratory and diagnostic data from electronic health records to support COVID-19 research efforts via the National COVID Cohort Collaborative (N3C) Data Enclave. As of August 2, 2022, the repository contains information from over 15 million patients (including 5.8 million COVID-19 positive patients) across the United States.

The N3C Data Enclave is organized into 3 levels of data with varying access restrictions:

• Synthetic dataset: Contains no protected health information (PHI). This is a statistically-comparable artificial dataset derived from the original dataset.
  • Can be requested by: Researchers from US-based or foreign institutions, and citizen scientists
• De-identified dataset: Contains no PHI. This dataset consists of real patient data with shifted dates of service and truncated ZIP codes of patients residing in areas with populations above 20,000.
  • Can be requested by: Researchers from US-based or foreign institutions
• Limited Data Set (LDS): Contains 2 PHI elements (dates of service and patient ZIP code). This dataset consists of real patient data.
  • Can be requested by: Researchers from US-based institutions only

Dataset

This dataset was created by Polina Stepanenko

Released under Database: Open Database, Contents: Database Contents

Contents

Ooo Nyu Stone Export Import Data. Follow the Eximpedia platform for HS code, importer-exporter records, and customs shipment details.